There are many instances where it may be desirable to have thousands of measurements of a resource (e.g. a natural resource) in many different physical locations. For example, the ability to measure the cleanliness of air or water would be useful, but the ability to measure the cleanliness of air or water with thousands, or perhaps millions, of data points across a wide geographical region in many aspects would be even more useful and important.
Normally, these types of mass measurements would be difficult and costly. In other words, taking measurements at a fine granularity (i.e., a small physical distance between each measurement) but having the entire scope of the measurements spanning a large distance (i.e., spanning a city, region, country, etc.) might require tens of thousands of sensor devices and a large logistical deployment operation.
Additionally, if the sensors simply stored captured measurements internally, it could very well be impracticable to have thousands of sensors each take data over a period of time and then require people to manually gather the data from each sensor. On the other hand, designing each sensor to be able to wirelessly send back data would increase the cost of the stand-alone sensor device as a whole. For example, when thousands of sensors might be needed to gain an accurate picture of an hour-by-hour change in the air quality at a detailed level across a large city such as Los Angeles or Shanghai, the cost of the sensor array may become prohibitive.
Currently, air and water quality sensor devices generally are bulky and include display panel(s), keys for user input, batteries or AC adapters, storage media to store the measurement data, as well as a processor and memory to process the measurement data taken.